Insect genomics to reduce phytosanitary product usage.

Through its long term collaboration with INRAE IGEPP, GenScale is involved in various genomic projects in the field of agricultural research. In particular, we participate in the genome assembly and analyses of some major agricultural pests or their natural ennemies such as parasitoids. The long term objective of these genomic studies is to develop control strategies to limit population outbreaks and the dissemination of plant viruses they frequently transmit, while reducing the use of phytosanitary products.

Energy efficient genomic computation through Processing-in-Memory.

All current computing platforms are designed following the von Neumann architecture principles, originated in the 1940s, that separate computing units (CPU) from memory and storage. Processing-in-memory (PIM) is expected to fundamentally change the way we design computers in the near future. These technologies consist of processing capability tightly coupled with memory and storage devices. As opposed to bringing all data into a centralized processor, which is far away from the data storage and is bottlenecked by the latency (time to access), the bandwidth (data transfer throughput) to access this storage, and energy required to both transfer and process the data, in-memory computing technologies enable processing of the data directly where it resides, without requiring movement of the data, thereby greatly improving the performance and energy efficiency of processing of massive amounts of data potentially by orders of magnitude. This technology is currently under test in GenScale with a revolutionary memory component developed by the UPMEM company. Several genomic algorithms have been parallelized on UPMEM systems, and we demonstrated significative energy gains compared to FPGA or GPU accelerators. For comparable performances (in terms of execution time) on large scale genomics applications, UPMEM PIM systems consume 3 to 5 times less energy.

7.1.1 kmtricks

• Keywords:
  High throughput sequencing, Indexing, K-mer, Bloom filter, K-mers matrix
• Functional Description:
  kmtricks is a tool suite built around the idea of k-mer matrices. It is designed for counting k-mers, and constructing bloom filters or counted k-mer matrices from large and numerous read sets. It takes as inputs sequencing data (fastq) and can output different kinds of matrices compatible with common k-mers indexing tools. The software is composed of several command line tools, a C++ library, and a C++ plugin system to extend its features.
• URL:
  https://­github.­com/­tlemane/­kmtricks
• Publication:
  hal-03166007
• Contact:
  Pierre Peterlongo
• Participants:
  Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.2 kmdiff

• Keywords:
  K-mer, K-mers matrix, GWAS
• Functional Description:
  Genome wide association studies elucidate links between genotypes and phenotypes. Recent studies point out the interest of conducting such experiments using k-mers as the base signal instead of single-nucleotide polymorphisms. kmdiff is a command line tool allowing efficient differential k-mer analyses on large sequencing cohorts.
• URL:
  https://­github.­com/­tlemane/­kmdiff
• Publication:
  hal-03885124
• Contact:
  Pierre Peterlongo
• Participants:
  Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.3 fimpera

• Keywords:
  Indexation, Data structures, K-mer, Bloom filter, Bioinfirmatics search sequence, Search Engine
• Functional Description:
  fimpera is a strategy for indexing and querying "hashtable-like" data structures named "AMQ" (for "Approximate Membership Query data structure"). When queried, those AMQs can yield false positives or overestimaed calls. fimpera reduces their false positive rate by two order of magnitude while reducing the overestimations, whithout introducing false negative and by speeding up queries.
• URL:
  https://­github.­com/­lrobidou/­fimpera
• Contact:
  Lucas Robidou
• Participants:
  Lucas Robidou, Pierre Peterlongo

7.1.4 SVJedi-graph

• Keywords:
  Structural Variation, Genotyping, High throughput sequencing, Sequence alignment
• Functional Description:
  SVJedi-graph is a structural variation (SV) genotyper for long read data. It constructs a variation graph to represent all alleles of all SVs given as input. This graph-based approach allows to better represent close and overlapping SVs. Long reads are then aligned to this graph and the genotype of each variant is estimated based on allele-specific alignment counts. SVJedi-graph takes as input a variant file (VCF), a reference genome (fasta) and a long read file (fasta/fastq) and outputs the initial variant file with an additional column containing genotyping information (VCF).
• URL:
  https://­github.­com/­SandraLouise/­SVJedi-graph
• Contact:
  Claire Lemaitre
• Participants:
  Claire Lemaitre, Sandra Romain

7.1.5 MTG-link

• Keywords:
  Bioinformatics, Genome assembly, Barcode, Linked-reads, Gap-filling
• Functional Description:
  MTG-Link is a local assembly tool dedicated to linked-read sequencing data. It leverages barcode information from linked-reads to assemble specific loci. Notably, the sequence to be assembled can be totally unknown (contrary to targeted assembly tools). It takes as input a set of linked-reads, the target flanking sequences and coordinates in GFA format and an alignment file in BAM format. It outputs the results in a GFA file.
• Release Contributions:
  MTG-Link can now be used for various local assembly use cases, such as intra-scaffold and inter-scaffold gap-fillings, as well as the reconstruction of the alternative allele of large insertion variants. It is also directly compatible with the following linked-reads technologies, given that the barcodes are reported using the "BX:Z" tag: 10X Genomics, Haplotagging, stLFR and TELL-Seq.
• URL:
  https://­github.­com/­anne-gcd/­MTG-Link
• Publications:
  hal-03073966, hal-03074227, hal-03441914, hal-03886951
• Contact:
  Claire Lemaitre
• Participants:
  Anne Guichard, Fabrice Legeai, Claire Lemaitre
• Partner:
  INRAE

7.1.6 QuickDeconvolution

• Keywords:
  High throughput sequencing, Genomics
• Functional Description:
  QuickDeconvolution deconvolutes a set of linked reads: QuickDeconvolution takes as input a linked reads dataset and adds an extension (-1, -2, -3...) to the barcodes, such that two reads with the same barcode and the same extension comes from the same genomic region.
• Release Contributions:
  This new versions implement a series of improvements that have been made in order to publish the paper.
• URL:
  http://­github.­com/­RolandFaure/­QuickDeconvolution
• Contact:
  Roland Faure
• Participants:
  Roland Faure, Dominique Lavenier

7.1.7 GraphUnzip

• Keywords:
  Genome assembly, Genome assembling, Haplotyping
• Functional Description:

  GraphUnzip untangles assembly graphs: GraphUnzip takes two input: 1) An assembly graph in GFA fromat, from an assembler 2) Data that can help untangling the graph: Hi-C, long reads or linked reads.

  GraphUnzip returns an untangled assembly graph, improving significantly the contiguity of the input assembly.

• Release Contributions:
  Brand new Hi-C algorithm now fit for publication. Great increase in accuracy and performance.
• URL:
  http://­github.­com/­nadegeGuiglielmoni/­GraphUnzip
• Contact:
  Roland Faure
• Participants:
  Roland Faure, Jean-François Flot, Nadège Guiglielmoni
• Partner:
  Université libre de Bruxelles

7.1.8 SeqFaiLR

• Keywords:
  Long reads, Sequencing error, Sequence alignment
• Functional Description:
  SeqFaiLR analyses Nanopore long reads sequencing error profiles. The algorithms have been designed for Nanopore data, but can be applied for other long read data. From raw reads and reference genomes, these scripts perform alignment and compute several analysis (low-complexity regions sequencing accuracy, GC bias, links between error rates and quality scores, and so on).
• URL:
  https://­github.­com/­cdelahaye/­SeqFaiLR
• Contact:
  Clara Delahaye
• Participants:
  Jacques Nicolas, Clara Delahaye

7.1.9 ORI

• Name:
  Oxford nanopore Reads Identification
• Keywords:
  Bioinformatics, Bloom filter, Spaced seeds, Long reads, ASP - Answer Set Programming, Bacterial strains
• Functional Description:
  ORI (Oxford nanopore Reads Identification) is a software using long nanopore reads to identify bacteria present in a sample at the strain level. There are two sub-parts in ORI: (1) the creation of the index containing the reference genomes of the interest species and (2) the query of this index with long reads from Nanopore sequencing in order to identify the strain(s).
• URL:
  https://­github.­com/­gsiekaniec/­ORI
• Contact:
  Jacques Nicolas
• Participants:
  Gregoire Siekaniec, Teo Lemane, Jacques Nicolas, Emeline Roux

7.1.10 Wisp

• Name:
  A Python application for bacterial families identification from long reads.
• Keywords:
  Nucleic Acids, Machine learning, Bioinformatics, Biodiversity, Genomic sequence, Omic data, Bacterial strains
• Scientific Description:
  Genomic and metagenomic data are flowing into microbiology thanks to advances in sequencing. In particular, we consider here ONT Minion long read data, that have a relatively high error rate. In this context, this work addresses a key problem, binning, which consists of grouping sequenced reads into taxonomically coherent sets. We have learned genomic signatures on two databases of microbial genomes and for various taxonomic levels, relying on a model of regression trees boosting, thanks to the XGBoost library. We used as attributes the frequencies of small k-mers (in range 4-6) on 10kb fragments sampled along the genomes. Each level of the taxonomy (until the family level) is predicted assuming the previous level is known. The prediction was made at the scale of single reads and groups of 400 reads, with a preprocessing step discarding low signifiance fragments. Overall, the level of accuracy achieved is very satisfactory, with known occasional issues such as for the Fusobacteria phylum and the Deltaproteobacteria group. Apart from the domain level (bacteria or archae), the most coherent taxonomic level seems to be that of the order. We designed a software suite, Wisp, covering database creation, error processing, creation of reports, quality analysis of predictions, using a user-friendly parameters interface.
• Functional Description:
  This tool is requiring some reference genomes, which it will index, to create a XGBoost model. Genomic fasta (.fna) files are the prefferd input style as of now. One can download custom genome dataset with NCBI accession numbers to create its own specific dataset and increase even more classifier accuracy.
• Release Contributions:
  Initial version
• Contact:
  Jacques Nicolas
• Participants:
  Jacques Nicolas, Siegfried Dubois

7.1.11 BlockDTW

• Name:
  Block Dynamic Time Warping
• Keywords:
  Alignment, Algorithm, Distance
• Functional Description:
  This software provides implementations of an O(nM+mN) and an O(nmk) time algorithm to compute pattern matching for DTW distance. We divide the dynamic programming matrix into blocks and improve the computation inside those, for further details see https://hal.archives-ouvertes.fr/hal-03763091/
• URL:
  https://­github.­com/­fnareoh/­DTW
• Contact:
  Garance Gourdel
• Participants:
  Garance Gourdel, Anne Driemel, Pierre Peterlongo, Tatiana Starikovskaya

7.1.12 PyRevSymG

• Name:
  Python Reverse Symmetric Graph
• Keywords:
  Directed graphs, Graph algorithmics, DNA sequencing
• Functional Description:
  Python3 API to store succession relationships between oriented fragments (in forward or reverse orientation) that have been sequenced from nucleotide sequence(s) in an oriented graph. For example, this API can be used for a genome assembly overlap-layout-consensus method.
• URL:
  https://­pypi.­org/­project/­revsymg/
• Contact:
  Victor Epain
• Participant:
  Victor Epain

7.1.13 DnarXiv

• Name:
  dnarXiv project platform
• Keywords:
  Biological sequences, Simulator, Sequence alignment, Error Correction Code
• Functional Description:
  The objective of DnarXiv is to implement a complete system for storing, preserving and retrieving any type of digital document in DNA molecules. The modules include the conversion of the document into DNA sequences, the use of error-correcting codes, the simulation of the synthesis and assembly of DNA fragments, the simulation of the sequencing and basecalling of DNA molecules, and the overall supervision of the system.
• URL:
  https://­gitlab.­inria.­fr/­dnarxiv
• Contact:
  Olivier Boulle
• Participants:
  Olivier Boulle, Dominique Lavenier
• Partners:
  IMT Atlantique, Université de Rennes 1

7.1.14 MOF-SEARCH

• Name:
  MOF-SEARCH
• Keywords:
  Bioinformatics, Alignment, Genomic sequence, Data compression
• Functional Description:
  A tool for rapid BLAST-like search among 661k sequenced bacteria on personal computers.
• URL:
  http://­github.­com/­karel-brinda/­mof-search
• Contact:
  Karel Brinda
• Participant:
  Karel Brinda
• Partners:
  European Bioinformatics Institute, HARVARD Medical School

8.1.1 Represent large sets of sequencing data with kmer matrices or bloom filters

Participants: Pierre Peterlongo, Téo Lemane.

When indexing large collections of short-read sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees  51, Mantis  50 BIGSI  49 and variants) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of kmers. kmers whose abundance is lower than a hard threshold are discarded. This representation approximates the desired set of all the non-erroneous kmers present in the sample. It has the precious advantage to index complete read sets composed of hundreds of millions reads with a few GB of memory or disk. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant kmers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. Additionally, existing tools able to generate matrices of counted kmers or collections of bloom filters have important running time.

In this context, we proposed kmtricks 16, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are 1/ an efficient method for jointly counting kmers across multiple samples, including a streamlined Bloom filter construction by directly counting, partitioning, and sorting hashes instead of kmers, which is approximately four times faster than state-of-the-art tools; 2/ a novel technique that takes advantage of joint counting to preserve low-abundant kmers present in several samples, improving the recovery of non-erroneous kmers. Our experiments highlight that this technique preserves around 8x more valid kmers than the usual yet crude filtering of low-abundance kmers in a large metagenomics dataset.

Using our workflow, we performed for the first time a massive-scale joint kmer counting and Bloom filter construction of a 6.5 terabases metagenomics collection, in under 50 GB of memory and 38 hours, which is at least 3.8 times faster than the next best alternative.

8.1.2 kmer-based genome wide association studies

Participants: Pierre Peterlongo, Téo Lemane.

Genome Wide Association Studies (GWAS) elucidate links between genotypes and phenotypes. Recent studies point out the interest of conducting such experiments using kmers as the base signal instead of single-nucleotide polymorphisms. We proposed a tool, called kmdiff, that performs differential kmer analyses on large sequencing cohorts in an order of magnitude less time and memory than previously possible 15.

8.1.3 Improvement of Approximate Membership Query data-structures with counts

Participants: Pierre Peterlongo, Lucas Robidou.

We proposed a novel computation method, called fimpera, consisting of a simple strategy for reducing the false-positive rate of any AMQ indexing all kmers from a set of sequences, along with their abundance information. This method decreases the false-positive rate of a counting Bloom filter by an order of magnitude while reducing the number of overestimated calls, as well as lowering the average difference between the overestimated calls and the ground truth. In addition, it slightly decreases the query run time. The fimpera method does not require any modification of the original counting Bloom filter, it does not generate false-negative calls, and it causes no memory overhead. The unique drawback is that fimpera yields a new kind of false positives and overestimated calls. However their amount is negligible. As a side note, for the algorithmic needs of the method, we also propose a novel generic algorithm for finding minimal values of a sliding window over a vector of x integers in $O\left(x\right)$ time with zero memory allocation 41.

8.1.4 Standardized and compact disk representation of sets of k-mers

Participants: Pierre Peterlongo, Téo Lemane.

8.2.1 Structural Variation genotyping with variant graphs

Participants: Claire Lemaitre, Sandra Romain.

One of the problems in Structural Variant (SV) analysis is the genotyping of variants. It consists in estimating the presence or absence of a set of known variants in a newly sequenced individual. Our team previously released SVJedi, one of the first SV genotypers dedicated to long read data. The method is based on linear representations of the allelic sequences of each SV. While this is very efficient for distant SVs, the method fails to genotype some closely located or overlapping SVs. To overcome this limitation, we present a novel approach, SVJedi-graph, which uses sequence graphs instead of linear sequences to represent the SVs.

In our method, we build a variation graph from a reference genome and a given set of SVs. The SV breakpoints are extracted and sorted. The genome sequence is then split at each breakpoint into non-overlapping fragments. Each fragment becomes a node in the graph, and edges are added between nodes to form the reference path of the genome as well as the alternative path for each SV. Additional nodes are added for insertions. The long reads are then mapped on the variation graph and the resulting alignments that overlap an edge (breakpoint) in the graph are used to estimate the most likely genotype for each SV.

Running SVJedi-graph on simulated sets of close deletions showed that the use of a variation graph was able to restore the genotyping quality on close and overlapping SVs. For instance, with a simulated set of deletions that had another deletion 0 to 50 bp apart, SVJedi-graph was able to genotype 99.6% of the deletions with an accuracy of 98.5%, compared to a genotyping rate of 78.9% and an accuracy of 97.3% with SVJedi on the same dataset. We tested our method on a "gold standard" datasets of Genome In A Bottle (Tier 1 SVs of human individual HG002), and obtained higher genotyping rates than SVJedi and a higher genotyping accuracy than other state of the art tools 45.

8.2.2 Deconvolution of linked-read sequencing data

Participants: Roland Faure, Dominique Lavenier.

Linked-read technologies, such as the 10X chromium system, use microfluidics to tag multiple short reads from the same long fragment (50–200 kb) with a small sequence, called a barcode. They are inexpensive and easy to prepare, combining the accuracy of short-read sequencing with the long-range information of barcodes. The same barcode can be used for several different fragments, which complicates the analyses. We have developed QuickDeconvolution (QD), a fast software for deconvolving a set of reads sharing a barcode, i.e. separating the reads from the different fragments. QD only takes sequencing data as input, without the need for a reference genome. We showed that QD outperforms existing software in terms of accuracy, speed and scalability, making it capable of deconvolving previously inaccessible data sets 12.

8.2.3 Local assembly with linked-read data

Participants: Anne Guichard, Fabrice Legeai, Claire Lemaitre.

Local assembly consists in reconstructing a sequence of interest from a sample of sequencing reads without having to assemble the entire genome, which is time and labor intensive. This is particularly useful when studying a locus of interest, for gap-filling in draft assemblies, as well as for alternative allele reconstruction of large insertion variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information, there is a lack of local assembly tools for linked-read data.

We present MTG-Link, a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions of each targeted locus. Our approach relies then on our tool MindTheGap 8 to perform local assembly of each locus with the read subsets. MTG-Link tests different parameters values for gap-filling, followed by an automatic qualitative evaluation of the assembly.

We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to successfully assemble large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants in a human genome and improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata 40.

8.2.4 Assembling unknown numbers of haplotypes

Participants: Roland Faure, Dominique Lavenier.

We are currently designing a software to provide phased assemblies from draft, purged assemblies. It implements a new method that takes as input a contig and the set of (high-error-rate) sequencing reads used to build it. Then it finds out whether the contig is actually a mix of several haplotypes, and if so outputs the haplotype-specific versions of this contig. To do so, SNPs are called rudimentarily allowing recurring pattern detection of variants among the reads. Unlike previously existing techniques, the method does not need as an input the number of expected haplotypes, as each recurring pattern of variants results in one group of reads. This makes the method especially useful to assemble polyploid species (common in plants and fishes), metagenomic samples, and even repeated regions 27 44.

8.2.5 Haplotype phasing of long reads for polyploid species

Participants: Clara Delahaye, Jacques Nicolas.

We are working on a binning problem, assigning the long reads of a sample to their native haplotype. While existing approaches rely on the use of a reference genome and known variants, we propose a method relying on long reads only, suited for the phasing of polyploid organisms. In order to compensate for the absence of reference genome, independent sub-problem instances are built from a restrained set of the longest reads acting as a pseudo-reference. The longest reads are phased and then used as anchors to map remaining reads, which are phased by maximizing their consistency with the phased anchors. By reasoning on the set of possible solutions, and integrating user preferences, it is possible to increase the robustness of results. We applied the phasing algorithm on a diploid, A. vaga (showing ancient tetraploid state) long read dataset for which a confident phased reference genome is available to evaluate the results. We also introduced an haplotig graph, which enables to explicitly point the regions of identity between haplotypes, as well as their differences.

This work has led to the PhD defense of Clara Delahaye at the end of this year 33.

8.2.6 Efficiently storing DNA fragments' succession relationships in a graph

Participants: Victor Epain, Rumen Andonov.

Assembling DNA fragments based on their overlaps remains the main assembly paradigm with long DNA fragments sequencing technologies, independently of the aim to resolve only one or several haplotypes. Since an overlap can be seen as a succession relationship between two oriented fragments, the directed graph structure has emerged as an appropriate data structure for handling overlaps. However, this graph paradigm does not appear to take advantage of the reverse symmetry of the orientated fragments and their overlaps, which is a result of blind DNA double-strand sequencing. Thus, the bi-directed graph paradigm was introduced in 1995 towards reducing the graph size by handling the reverse symmetry, and becomes since then the main graph paradigm used in assembly/scaffolding methods. Nevertheless, these two graph paradigms have never been contrasted before, and no implementations have been described. We present suitable data structures that are theoretically compared in terms of time and memory consumption in the context of the design of some basic graph algorithms. We also show that each one of the paradigms can be switched to another by slightly modifying their data structures.

These results are described in a submitted version for RECOMB2022 conference 37. They have been presented at DBS2022 workshop at Düsseldorf. An extended version can be found in 39.

One of the described graph implementations is available on a public released Python3 package 7.1.12

8.2.7 Optimal inverted repeats scaffolding for chloroplast genomes

Participants: Victor Epain, Rumen Andonov, Dominique Lavenier.

Here we describe a novel assembly approach for chloroplast genomes. It contains two modular steps. In the first step, based on the hypothesis that chloroplasts genomes are over-represented compared to the nuclear genome in the plant's cell, we assemble contigs with a De Bruijn graph based approach using short reads with a high k-mer coverage. Connections between oriented contigs are also provided here. The second step determines the order and the orientation of the contigs (scaffolding). Taking advantage of the knowledge that chloroplast genomes possess well studied circular structure, we develop a particular formulation of the scaffolding problem, called Nested Inverted Fragments Scaffolding. It aims at assembling highly conserved inverted repeats. We formulate it as an optimisation problem and we prove that it is NP-Complete. To solve the problem we propose and implement an integer linear programming formulation. We evaluate our method on a set of real instances (a benchmark of 42 chloroplast genomes) and show that it obtains notable achievements with respect to the quality of the results. To further estimate the performance of our scaffolding module, we test it on huge artificially created instances. The results demonstrate an excellent behaviour of our integer formulation as even very large instances have been solved at the first Branch and Bounds node.

These works have been presented at the ROADEF2022 conference 26 and at the JOBIM2022 conference 22. These results are described in a submitted version for ISCO2022 symposium 36 and in a submitted version for WABI2023 conference 38.

8.3.1 Storing the declaration of human rights on a single DNA molecule

Participants: Olivier Boulle, Dominique Lavenier, Julien Leblanc, Jacques Nicolas, Emeline Roux.

Today, the community consensus to store information on DNA is to use short single strand DNA (ssDNA) molecules. This approach has some limitations: encoding constraint, DNA stability, recovering DNA, sequencing technology, etc. To overcome them, we chose to store information on long double-strand DNA (dsDNA) molecules. Our demonstration consists in storing the first articles of the declaration of human rights (4.2 KByte text document) on a single DNA molecule 43.

Our approach is based on an ordered assembly of short oligonucleotides. The document is first split into small DNA fragments whose length are compatible with DNA synthetizers (about 60 nucleotides). A first assembly concatenates pools of 10 oligonucleotides to form double strand DNA molecules of approximatively 600 bp. A new round of ordered assembly takes these 600 bp molecules to build 6 kbp molecules. The last round assembles 5 of these molecules to form the final long DNA molecules supporting the full text.

To be able to build such long DNA molecules, we have developed a specific and systematic biotechnology protocol which, in interaction with our experimental platform (see next paragraph) should lead to a complete automation of the DNA writing process.

8.3.2 Experimental DNA storage platform

Participants: Olivier Boulle, Dominique Lavenier, Julien Leblanc, Jacques Nicolas, Emeline Roux.

The dnarXiv projects aims to explore various strategies for DNA storage. We have designed an experimental DNA storage platform allowing both to conduct real and/or in-silico experiments. The platform includes different modules generally used in the write/read DNA storage process: encoding, synthesis, molecule design, storage, molecule selection, genomic data processing, decoding. This is a flexible environment for testing various approaches simply by substituting new modules to the existing ones 25.

8.3.3 DNA data storage security

Participants: Dominique Lavenier.

In this work, we are interested in securing archived data. DNA storage being a new technology, there is an opportunity to integrate this critical aspect at the biological level, contrarily to what has been done for electronical storage means. In fact, information must be secured at every step of the DNA data storage chain. Data integrity and confidentiality are among the main issues with threats like data modification (e.g. writing of new data) or the theft of the DNA storage support by an attacker. Herein, we propose a solution for writing encrypted data onto synthetic DNA molecules considering DNA synthesis and the error-correction code constraints. Indeed, DNA sequences should conform to structural constraints dictated by this biological process and sequencing 2442.

8.3.4 Exploring DNA synthesis and sequencing semiconductor technologies

Participants: Dominique Lavenier.

Within the dnarXiv project, we explored the different ways of performing synthesis and sequencing steps and more specifically how the reading and writing processes can be implemented on semiconductor devices. 28

Sequence Alignment. 

The aim is to compute consensus sequence from long reads. It implies to make many pairwise comparisons based on dynamic programming algorithms. KSW2 is used for that purpose. We are currently implementing a processing-in-memory parallel strategy of KSW2 to optimize the full process. The last measurements show a speed-up ranging from 5 to 10 compared to an optimized openMP implementation.

Bacterial genome comparison. 

The goal is to compare large sets of bacterial genomes to estimate their similarities (as DASHING2). Genome sketches are first computed, and distances are computed based on these sketches. We face a massive parallelism that efficiently exploit on the UPMEM server. Speed-up around 20 is expected. Experimentation are done on a set of 661 000 bacterial genomes.

Protein sequence alignment. 

We are currently implementing a blast-like algorithm to scan large protein databanks. The protein bank is split over the Processing-in-Memory components. The query is broadcasted to all DRAM processing units (DPU) which send back alignments to the host processor. The parallelization of the algorithm is achieved and performance measurements will start in January 2023.

8.5.1 Evaluation of metagenomic software: the second round of CAMI challenges

Participants: Claire Lemaitre, Pierre Peterlongo.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here 5,002 results by 76 program versions were analyzed. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses 18.

GenScale team members have participated in this competition: we runned our genome assembly software  4 on the CAMI data and provided the results and full pipelines to the evaluation team.

8.5.2 Introduction to bioinformatics methods for metagenomic and metatranscriptomic analyses

Participants: Claire Lemaitre.

In this book chapter, we review the different bioinformatics analyses that can be performed on metagenomics and metatranscriptomics data. We present the differences of this type of data compared to standard genomics data and highlight the methodological challenges that arise from it. We then present an overview of the different methodological approaches and tools to perform various analyses such as taxonomic annotation, genome assembly and binning and de novo comparative genomics  31.

8.6.1 Pattern matching under DTW distance

Participants: Garance Gourdel, Pierre Peterlongo.

We considered the problem of pattern matching under the dynamic time warping (DTW) distance motivated by potential applications in the analysis of biological data produced by the third generation sequencing. To measure the DTW distance between two strings, we must “warp” them, that is, double some letters in the strings to obtain two equal-length strings, and then sum the distances between the letters in the corresponding positions. When the distances between letters are integers, we show that for a pattern $P$ with $m$ runs (a run being a maximal set of consecutive letters) and a text $T$ with $n$ runs there is an $O\left(kmn\right)$-time algorithm that computes all locations where the DTW distance from $P$ to $T$ is at most $k$ 23.

8.6.2 Streaming Regular Expression Membership and Pattern Matching

Participants: Garance Gourdel.

A regular expression R is a formalism for compactly describing a set of strings, built recursively from single characters using three operators: concatenation, union, and Kleene star. In this paper we study membership and pattern matching of regular expressions in the streaming setting (where the pattern can be preprocessed, then the characters of the text are seen one at a time, and all space must be accounted for). In general, we cannot hope for a streaming algorithm with space complexity smaller than the length of R for either variant of regular expression search. The main contribution of this paper is that we identify the number of unions and Kleene stars, denoted by $d$, as the parameter that allows for an efficient streaming algorithm. We design general randomised Monte Carlo algorithms for both problems that use $O(d^3$ polylog $n)$ space in the streaming setting 30.

8.7.1 Comparing seawater metagenomes from the Tara ocean project

Participants: Claire Lemaitre, Pierre Peterlongo.

GenScale team members have participated to the developement of algorithms that enable such large scale sequencing data comparisons, and they provided their expertise regarding the results and their analyses.

8.7.2 Genomics and transcriptomics of Brassicaceae plants and agro-ecosystem insects

Participants: Fabrice Legeai.

Through its long term collaboration with INRAE IGEPP, and its support to the BioInformatics of Agroecosystems Arthropods platform, GenScale is involved in various genomic and transcriptomics projects in the field of agricultural research. In particular, we participated in the genome assembly and analyses of some major agricultural pests or their natural ennemies such as parasitoids. In most cases, the genomes and their annotations were hosted in the BIPAA information system, allowing collaborative curation of various set of genes and leading to novel biological findings 17, 10, 13, 21.

8.7.3 First chromosome scale genomes of ithomiine butterflies

Participants: Fabrice Legeai, Claire Lemaitre.

In the framework of a former ANR project (SpecRep 2014-2019), we worked on de novo genome assembly of several ithomiine butterflies. Due to their high heterozygosity level and to sequencing data of various quality, this was a challenging task and we tested numerous assembly tools. Finally, this work led to the generation of high-quality, chromosome-scale genome assemblies for two Melinaea species, M. marsaeus and M. menophilus, and a draft genome of the species Ithomia salapia. We obtained genomes with a size ranging from 396 Mb to 503 Mb across the three species and scaffold N50 of 40.5 Mb and 23.2 Mb for the two chromosome-scale assemblies. Various genomics and comparative genomics analyses were performed and revealed notably independent gene expansions in ithomiines and particularly in gustatory receptor genes.

These three genomes constitute the first reference genomes for the ithomiine butterflies (Nymphalidae: Danainae), which represent the largest known radiation of Müllerian mimetic butterflies and dominate by number the mimetic butterfly communities. This is therefore a valuable addition and a welcome comparison to existing biological models such as Heliconius, and will enable further understanding of the mechanisms of mimetism and adaptation in butterflies 14.

8.7.4 Genomics of a lactic acid bacterium of industrial and health interest

Participants: Jacques Nicolas, Emeline Roux.

Streptococcus thermophilus is a bacterium widely used in the dairy industry as well as in many traditional fermented products. In addition, S. thermophilus exhibits functionalities favorable to Human health. We investigate the main health-promoting properties of S. thermophilus and study their intra-species diversity within a collection of representative strains (around 80 genome sequences of strains, 30 of which were sequenced and assembled during Gregoire Siekaniec's thesis). Some functions are widely conserved among isolates (e.g., folate production, degradation of lactose) suggesting their central role for the species, while others (e.g., catabolism of galactose, production of bioactive peptides) are strain-specific. A better understanding of the health-promoting properties and the genomic and genetic diversity within S. thermophilus species could facilitate the selection and development of fermented products with health-promoting properties 20, 46.

10.1.1 H2020 projects

10.1.2 Other european programs/initiatives

10.2.1 PEPR

10.2.2 ANR

10.2.3 Inria Exploratory Action

10.3.1 Labex Cominlabs

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• C. Lemaitre, "Methodological challenges of Structural Variation characterization and the particular case of insertions", keynote speaker at the meeting reads2genpop : From sequence reads to genomes and populations, Paris, Sept. 2022.
• C. Lemaitre, "Finding structural variants with sequencing data: the difficult case of insertions", Keynote speaker at the annual meeting of GDR AIEM and Alphy working group (GDR BIM), Rennes, March 2022.
• D. Lavenier, "DNA Storage", IDA 2022, International Symposium on Intelligent Data Analysis, Rennes, Juillet 2022
• P. Peterlongo, "Swim in the data tsunami", JOBIM 2022, Rennes, Juillet 2022, keynote speaker
• K. Brinda, "The tree of life enables efficient and robust compression and search of microbes", JOBIM 2022 minisymposium, Rennes, July 2022.

11.1.5 Leadership within the scientific community

• Members of the Scientific Advisory Board of the GDR BIM (National Research Group in Molecular Bioinformatics) [P. Peterlongo, C. Lemaitre]
• Animator of the Sequence Algorithms axis (seqBIM GT) of the BIM and IM GDRs (National Research Groups in Molecular Bioinformatics and Informatics and Mathematics respectively) (170 french participants) [C. Lemaitre]
• Animator of the INRAE Center for Computerized Information Treatment "BARIC" [F. Legeai]

11.1.6 Scientific expertise

• Scientific expert for the DGRI (Direction générale pour la recherche et l’innovation) from the Ministère de l’Enseignement Supérieur, de la Recherche et de l’Innovation (MESRI) [D. Lavenier]

11.1.7 Research administration

• Corresponding member of COERLE (Inria Operational Committee for the assessment of Legal and Ethical risks). Participation to the ethical group of IFB (French Elixir node, Institut Français de Bioinformatique) [J. Nicolas]
• Member of the steering committee of the INRAE BIPAA Platform (BioInformatics Platform for Agro-ecosystems Arthropods) [P. Peterlongo]
• Institutional delegate representative of INRIA in the GIS BioGenOuest regrouping all public research platforms in Life Science in the west of France (régions Bretagne/ Pays de Loire) [J. Nicolas]
• Scientific Advisor of The GenOuest Platform (Bioinformatics Resource Center of BioGenOuest) [J. Nicolas]
• Chair of the committee in charge of all the temporary recruitments (“Commission Personnel”) at Inria Rennes-Bretagne Atlantique and IRISA [D. Lavenier]

11.2.1 Teaching administration

• In charge of the master's degree "Nutrition Sciences des Aliments" (NSA) of University of Rennes 1 (45 students) [E. Roux]

11.2.2 Teaching

• Licence : R. Andonov, Models and Algorithms in Graphs, 100h, L3, Univ. Rennes 1, France.
• Licence : E. Roux, biochemistry, 90h, L1 and L3, Univ. Rennes 1, France.
• License: L. Robidou, K. Hannoush, Principles of Computer Systems, 48h, L1, Univ. Rennes 1, France.
• License: L. Robidou, Outils bureautiques pour le statisticien, 6h, L1, Ensai, France
• Master : R. Andonov, Operations Research (OR), 82h, M1 Miage, Univ. Rennes 1, France.
• Master : R. Andonov, Optimisation Techniques in Bioinformatics, 18h, M2, Univ. Rennes 1, France.
• Master : C. Lemaitre, P. Peterlongo, S. Romain, Algorithms on Sequences, 52h, M2, Univ. Rennes 1, France.
• Master : C. Lemaitre, S. Romain, Bioinformatics of Sequences, 40h, M1, Univ. Rennes 1, France.
• Master : P. Peterlongo, Experimental Bioinformactics, 24h, M1, ENS Rennes, France.
• Master : E. Roux, biochemistry, 120h, M1 and M2, Univ. Rennes 1, France.
• Aggreg: D. Lavenier, Computer Architecture, 20h, ENS Rennes

11.2.3 Supervision

• PhD: C. Delahaye, haplotype phasing from long reads with ASP: a flexible optimization approach 33, defended: 15/12/2022, J. Nicolas.
• PhD: T. Lemane, unbiased detection of neurodegenerative structural variants using k-mer matrices 34, defended: 16/12/2022, P. Peterlongo.
• PhD: K. Da Silva, Identification and quantification of microbial strains in metagenomic samples using variation graphs 35. 08/03/2022, P. Peterlongo.
• PhD in progress: V. Epain, genome assembly with long reads, 01/10/2020, R. Andonov,D. Lavenier, JF Gibrat.
• PhD in progress: G. Gourdel, Sketch-based approaches to processing massive string data, 01/09/2020, P. Peterlongo, T. Starikovskaya.
• PhD in progress: L. Robidou, Search engine for genomic sequencing data, 01/10/2020, P. Peterlongo.
• PhD in progress: S. Romain, Genome graph data structures for Structural Variation analyses in butterfly genomes, 01/09/2021, C. Lemaitre, F. Legeai.
• PhD in progress: K. Hannoush, Pan-genome graph update strategies, 01/09/2021, P. Peterlongo, C. Marchet.
• PhD in Progress: R. Faure, Recovering end-to-end phased genomes, 01/10/2021, D. Lavenier, J-F. Flot.
• PhD in progress: M. Mognol, Processing-in-Memory, 01/04.2022, D. Lavenier.

11.2.4 Juries

• President of PhD thesis jury.
  • B. Churcheward, Univ. Nantes, Dec 2022 [D. Lavenier]
• Member of PhD thesis jury:
  • Y. Shibuya, Univ. Gustave Eiffel, Nov 2022 [K. Brinda]
  • C. Delahaye, Univ. Rennes, Dec 2022 [J. Nicolas, D. Lavenier]
• Member of PhD thesis committee:
  • Rick Wertenbroek, Univ. Lausanne [D. Lavenier]
  • Xavier Pic, Univ. Nice [D. Lavenier]
  • Léo de La Fuente, Univ. Rennes [D. Lavenier]
  • Khodor Hannoush, Univ. Rennes [K. Brinda]

11.3.1 Articles and contents

• Short Movie "Patatogène", presented at Sciences en Courts, a local contest of popularization short movies made by PhD students (youtube video) [R. Faure, K. Hannoush, S. Romain]
• Popularization article in Interstices "Comment la bioinformatique a résolu le puzzle du génome du SARS-CoV-2" (link) 47 [C. Lemaitre]
• Popularization article in Interstices "Décoder le génome : vers la compréhension du fonctionnement du SARS-CoV-2" 48 (link) [C. Lemaitre]
• Book chapter on genome assembly, in "From Sequences to Graphs: Discrete Methods and Structures for Bioinformatics", 2022 32 [D. Lavenier]

11.3.2 Education

• Chiche! 4 interventions in high school classes to make high school students aware of research careers in the digital sector [P. Peterlongo]

International journals

• 10 articleY.Y Aigu, S.Stéphanie Daval, K.Kévin Gazengel, N.Nathalie Marnet, C.C Lariagon, A.Anne Laperche, F.F Legeai, M. J.Maria J Manzanares-Dauleux and A.Antoine Gravot. Multi-omic investigation of low-nitrogen conditional resistance to clubroot reveals Brassica napus genes involved in nitrate assimilation.Frontiers in Plant Science132022, 790563
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• 11 articleY.Yoann Dufresne, T.Teo Lemane, P.Pierre Marijon, P.Pierre Peterlongo, A.Amatur Rahman, M.Marek Kokot, P.Paul Medvedev, S.Sebastian Deorowicz and R.Rayan Chikhi. The K-mer File Format: a standardized and compact disk representation of sets of k-mers.Bioinformatics3818September 2022, 4423-4425
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• 12 articleR.Roland Faure and D.Dominique Lavenier. QuickDeconvolution: fast and scalable deconvolution of linked-read sequencing data.Bioinformatics AdvancesSeptember 2022, 1-8
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• 13 articleE.Estelle Fiteni, K.Karine Durand, S.Sylvie Gimenez, R. L.Robert L. Meagher Jr., F.Fabrice Legeai, G. J.Gael J. Kergoat, N.Nicolas Nègre, E.Emmanuelle dAlençon and K. W.Ki Woong Nam. Host-plant adaptation as a driver of incipient speciation in the fall armyworm ( Spodoptera frugiperda ).BMC Ecology and Evolution22November 2022, 133
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• 14 articleJ.Jérémy Gauthier, J.Joana Meier, F.Fabrice Legeai, M.Melanie McClure, A.Annabel Whibley, A.Anthony Bretaudeau, H.Hélène Boulain, H.Hugues Parrinello, S. T.Sam T. Mugford, R.Richard Durbin, C.Chenxi Zhou, S.Shane McCarthy, C. W.Christopher W. Wheat, F.Florence Piron-Prunier, C.Christelle Monsempes, M.Marie‐Christine François, P.Paul Jay, C.Camille Noûs, E.Emma Persyn, E.Emmanuelle Jacquin-Joly, C.Camille Meslin, N.Nicolas Montagné, C.Claire Lemaitre and M.Marianne Elias. First chromosome scale genomes of ithomiine butterflies (Nymphalidae: Ithomiini): comparative models for mimicry genetic studies.Molecular Ecology Resources2023
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• 15 articleT.Téo Lemane, R.Rayan Chikhi and P.Pierre Peterlongo. kmdiff, large-scale and user-friendly differential k-mer analyses.BioinformaticsOctober 2022, 1-3
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• 16 articleT.Téo Lemane, P.Paul Medvedev, R.Rayan Chikhi and P.Pierre Peterlongo. kmtricks: Efficient construction of Bloom filters for large sequencing data collections.Bioinformatics AdvancesApril 2022
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• 17 articleC.Camille Meslin, P.Pauline Mainet, N.Nicolas Montagné, S.Stéphanie Robin, F.Fabrice Legeai, A.Anthony Bretaudeau, J. S.J Spencer Johnston, F. A.Fotini A. Koutroumpa, E.Emma Persyn, C.Christelle Monsempès, M.-C.Marie-Christine François and E.Emmanuelle Jacquin-Joly. Spodoptera littoralis genome mining brings insights on the dynamic of expansion of gustatory receptors in polyphagous noctuidae.G3128June 2022, jkac131
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• 18 articleF.Fernando Meyer, A.Adrian Fritz, Z.-L.Zhi-Luo Deng, D.David Koslicki, T. R.Till Robin Lesker, A.Alexey Gurevich, G.Gary Robertson, M.Mohammed Alser, D.Dmitry Antipov, F.Francesco Beghini, D.Denis Bertrand, J.Jaqueline Brito, C. T.C. Titus Brown, J.Jan Buchmann, A.Aydin Buluç, B.Bo Chen, R.Rayan Chikhi, P.Philip Clausen, A.Alexandru Cristian, P. W.Piotr Wojciech Dabrowski, A.Aaron Darling, R.Rob Egan, E.Eleazar Eskin, E.Evangelos Georganas, E.Eugene Goltsman, M.Melissa Gray, L. H.Lars Hestbjerg Hansen, S.Steven Hofmeyr, P.Pingqin Huang, L.Luiz Irber, H.Huijue Jia, T. S.Tue Sparholt Jørgensen, S.Silas Kieser, T.Terje Klemetsen, A.Axel Kola, M.Mikhail Kolmogorov, A.Anton Korobeynikov, J.Jason Kwan, N.Nathan Lapierre, C.Claire Lemaitre, C.Chenhao Li, A.Antoine Limasset, F.Fabio Malcher-Miranda, S.Serghei Mangul, V.Vanessa Marcelino, C.Camille Marchet, P.Pierre Marijon, D.Dmitry Meleshko, D.Daniel Mende, A.Alessio Milanese, N.Niranjan Nagarajan, J.Jakob Nissen, S.Sergey Nurk, L.Leonid Oliker, L.Lucas Paoli, P.Pierre Peterlongo, V.Vitor Piro, J.Jacob Porter, S.Simon Rasmussen, E.Evan Rees, K.Knut Reinert, B.Bernhard Renard, E. M.Espen Mikal Robertsen, G.Gail Rosen, H.-J.Hans-Joachim Ruscheweyh, V.Varuni Sarwal, N.Nicola Segata, E.Enrico Seiler, L.Lizhen Shi, F.Fengzhu Sun, S.Shinichi Sunagawa, S. J.Søren Johannes Sørensen, A.Ashleigh Thomas, C.Chengxuan Tong, M.Mirko Trajkovski, J.Julien Tremblay, G.Gherman Uritskiy, R.Riccardo Vicedomini, Z.Zhengyang Wang, Z.Ziye Wang, Z.Zhong Wang, A.Andrew Warren, N. P.Nils Peder Willassen, K.Katherine Yelick, R.Ronghui You, G.Georg Zeller, Z.Zhengqiao Zhao, S.Shanfeng Zhu, J.Jie Zhu, R.Ruben Garrido-Oter, P.Petra Gastmeier, S.Stephane Hacquard, S.Susanne Häußler, A.Ariane Khaledi, F.Friederike Maechler, F.Fantin Mesny, S.Simona Radutoiu, P.Paul Schulze-Lefert, N.Nathiana Smit, T.Till Strowig, A.Andreas Bremges, A.Alexander Sczyrba and A. C.Alice Carolyn Mchardy. Critical Assessment of Metagenome Interpretation: the second round of challenges.Nature Methods194April 2022, 429-440
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• 19 articleD.Daniel Richter, R.Romain Watteaux, T.Thomas Vannier, J.Jade Leconte, P.Paul Frémont, G.Gabriel Reygondeau, N.Nicolas Maillet, N.Nicolas Henry, G.Gaëtan Benoit, A.Antonio Fernandez-Guerra, S.Samir Suweis, R.Romain Narci, C.Cédric Berney, D.Damien Eveillard, F. F.Frédérick F. Gavory, L.Lionel Guidi, K.Karine Labadie, E.Eric Mahieu, J.Julie Poulain, S.Sarah Romac, S.Simon Roux, C.Céline Dimier, S.Stefanie Kandels, M.Marc Picheral, S.Sarah Searson, S.Stéphane Pesant, J.-M.Jean-Marc Aury, J.Jennifer Brum, C.Claire Lemaitre, E.Eric Pelletier, P.Peer Bork, S.Shinichi Sunagawa, L.Lee Karp-Boss, C.Chris Bowler, M.Matthew Sullivan, E.Eric Karsenti, M.Mahendra Mariadassou, I.Ian Probert, P.Pierre Peterlongo, P.Patrick Wincker, C.Colomban de Vargas, M.Maurizio Ribera d'Alcalà, D.Daniele Iudicone and O.Olivier Jaillon. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems.eLifeAugust 2022
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• 20 articleE.Emeline Roux, A.Aurélie Nicolas, F.Florence Valence, G.Grégoire Siekaniec, V.Victoria Chuat, J.Jacques Nicolas, Y.Yves Le Loir and E.Eric Guédon. The genomic basis of the Streptococcus thermophilus health-promoting properties.BMC Genomics23March 2022, 1-23
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• 21 articleS.Sudeeptha Yainna, W. T.Wee Tek Tay, K.Karine Durand, E.Estelle Fiteni, F.Frédérique Hilliou, F.Fabrice Legeai, A.-L.Anne-Laure Clamens, S.Sylvie Gimenez, R.R. Asokan, C.C. Kalleshwaraswamy, S.Sharanabasappa Deshmukh, R.Robert Meagher, C.Carlos Blanco, P.Pierre Silvie, T.Thierry Brévault, A.Anicet Dassou, G.Gael Kergoat, T.Thomas Walsh, K.Karl Gordon, N.Nicolas Nègre, E.Emmanuelle d'Alençon and K.Kiwoong Nam. The evolutionary process of invasion in the fall armyworm (Spodoptera frugiperda).Scientific Reports121December 2022, 21063
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International peer-reviewed conferences

• 22 inproceedingsV.Victor Epain, R.Rumen Andonov and D.Dominique Lavenier. Optimal Scaffolding for Chloroplasts' Inverted Repeats.JOBIM2022Rennes, FranceJuly 2022
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• 23 inproceedingsG.Garance Gourdel, A.Anne Driemel, P.Pierre Peterlongo and T.Tatiana Starikovskaya. Pattern matching under DTW distance.String Processing and Information Retrieval - 29th International Symposium, SPIRE 2022, Concepcion, Chile, November 8-10, 2022, Proceedings. Lecture Notes in Computer Science SpringerSPIRE 2022 - 29th International Symposium on String Processing and Information RetrievalLecture Notes in Computer Science13617Concepción, ChileNovember 2022, 315--330
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Conferences without proceedings

• 24 inproceedingsC.Chloé Berton, G.Gouenou Coatrieux and D.Dominique Lavenier. A first proposal for secure data storage into DNA molecules compliant with biological constraints.DSMM 2022 - 1st International Conference on Data Storage in Molecular MediaVirtual, FranceMarch 2022
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• 25 inproceedingsO.Olivier Boullé and D.Dominique Lavenier. Experimental DNA storage platform.DSMM 2022 - 1st International Conference on Data Storage in Molecular MediaVirtual, FranceMarch 2022, 1-1
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• 26 inproceedingsV.Victor Epain and R.Rumen Andonov. Linear integer programming approach for chloroplast genome scaffolding.23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionLyon, France1-2February 2022
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• 27 inproceedingsR.Roland Faure, J.-F.Jean-François Flot and D.Dominique Lavenier. HairSplitter: assembling long reads in an unknown number of haplotypes.SeqBIM 2022 - Journées sur les Séquences en Bioinformatique, Informatique et MathématiquesBordeaux, FranceNovember 2022
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• 28 inproceedingsD.Dominique Lavenier. DNA Storage: Synthesis and Sequencing Semiconductor Technologies.IEDM 2022 - 68th Annual IEEE International Electron Devices MeetingSan Francisco, United StatesIEEEDecember 2022, 1-4
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• 29 inproceedingsD.Dominique Lavenier. Processing-in-Memory to speed up NGS analysis.SFA²F 2022 - Sequencing to Function: Analysis and Application for the FutureSanta Fe, United StatesJune 2022
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Scientific book chapters

• 30 inbookB.Bartłomiej Dudek, P.Paweł Gawrychowski, G.Garance Gourdel and T.Tatiana Starikovskaya. Streaming Regular Expression Membership and Pattern Matching.Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Society for Industrial and Applied MathematicsJanuary 2022, 670-694
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• 31 inbookC.Cervin Guyomar and C.Claire Lemaitre. Metagenomics and Metatranscriptomics.From Sequences to Graphs: Discrete Methods and Structures for BioinformaticsISTEOctober 2022
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• 32 inbookD.Dominique Lavenier. Genome Assembly.From Sequences to Graphs: Discrete Methods and Structures for Bioinformatics - SCIENCES - Bioinformatics - ISTE WileyOctober 2022
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Doctoral dissertations and habilitation theses

• 33 thesisC.Clara Delahaye. Haplotype phasing from long reads with ASP: a flexible optimization approach.Université rennes 1December 2022
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• 34 thesisT.Téo Lemane. Indexing and analysis of large sequencing collections using kmer matrices.Université de Rennes 1 (UR1)December 2022
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• 35 thesisK.Kévin da Silva. Identification and quantification of microbial strains in metagenomic samples using variation graphs.Université de Rennes 1March 2022
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Reports & preprints

• 36 miscV.Victor Epain and R.Rumen Andonov. Integer Programming Approach for Nested Pairs Genome Scaffolding.March 2022
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• 37 miscV.Victor Epain and R.Rumen Andonov. Overlap Graphs for Assembling and Scaffolding Algorithms: Paradigm Review and Implementation Proposals.October 2022
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• 38 miscV.Victor Epain, D.Dominique Lavenier and R.Rumen Andonov. Inverted Repeats Scaffolding for a Dedicated Chloroplast Genome Assembler.June 2022
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• 39 miscV.Victor Epain. Overlap Graph for Assembling and Scaffolding Algorithms: Paradigm Review and Implementation Proposals.2022
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• 40 miscA.Anne Guichard, F.Fabrice Legeai, D.Denis Tagu and C.Claire Lemaitre. MTG-Link: leveraging barcode information from linked-reads to assemble specific loci.September 2022
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• 41 miscL.Lucas Robidou and P.Pierre Peterlongo. fimpera: drastic improvement of Approximate Membership Query data-structures with counts.December 2022
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Other scientific publications

• 42 inproceedingsC.Chloé Berton, G.Gouenou Coatrieux and D.Dominique Lavenier. Secure data storage into DNA molecules compliant with biological constraints: Ensuring the confidentiality of data stored into DNA molecules.RITS 2022 - Recherche en Imagerie et Technologies pour la SantéBrest, FranceMay 2022, 1-1
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• 43 inproceedingsG.Gouenou Coatrieux, C.Chloé Berton, E.Elsa Dupraz, B.Belaid Hamoum, T.Thomas Derrien, Y.Yann Audic, D.Dominique Lavenier, J.Jacques Nicolas, J.Julien Leblanc, O.Olivier Boullé and E.Emeline Roux. Storing the declaration of human rights on one data DNA molecule.CominLabs day 2022Rennes, FranceOctober 2022, 1-1
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• 44 inproceedingsR.Roland Faure, J.-F.Jean-François Flot and D.Dominique Lavenier. Hairsplitter: Separating noisy long reads into an unknown number of haplotypes.Genome Informatics 2022London / Virtual, United KingdomSeptember 2022, 1-1
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• 45 inproceedingsS.Sandra Romain and C.Claire Lemaitre. SVJedi-graph: genotyping close and overlapping structural variants with a variation graph and long-reads.JOBIM 2022 - Journées Ouvertes en Biologie, Informatique et MathématiquesRennes, FranceJuly 2022
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• 46 inproceedingsE.Emeline Roux, A.Aurélie Nicolas, F.Florence Valence, G.Grégoire Siekaniec, V.Victoria Chuat, J.Jacques Nicolas, Y.Yves Le Loir and E.Eric Guédon. The genomic basis of the Streptococcus thermophilus health-promoting properties.JOBIM 2022 - Les journées Ouvertes en Biologie, Informatique et MathématiquesRennes, FranceJuly 2022, 1-1
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Scientific popularization

• 47 articleC.Claire Lemaitre, M.Mikaël Salson and H.Hélène Touzet. Comment la bioinformatique a résolu le puzzle du génome du SARS-CoV-2.IntersticesApril 2022
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• 48 articleH.Hélène Touzet, M.Mikaël Salson and C.Claire Lemaitre. Décoder le génome : vers la compréhension du fonctionnement du SARS-CoV-2.IntersticesApril 2022
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